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Multi Agent Collaborative Search Algorithm with Adaptive Weights
  • +1
  • Li Cao,
  • Maocai Wang,
  • Guangming Dai,
  • Max Vassile
Li Cao
China University of Geosciences College of Computer Science
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Maocai Wang
China University of Geosciences College of Computer Science

Corresponding Author:[email protected]

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Guangming Dai
China University of Geosciences College of Computer Science
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Max Vassile
University of Strathclyde Department of Mechanical and Aerospace Engineering
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Abstract

This paper presents a new Multi Agent Collaborative Search Algorithm with Adaptive Weights (named MACSAW). MACS is a memetic scheme for multi-objective optimization which contains two kind of actions, the local actions and social actions. The former explore the neighborhood of some virtual agents and the latter push the individual towards the Pareto front. On the base of the latest version of MACS, MACS2.1, we improve the old algorithm from three direction. First, a new kind of utility function is introduced to enhance the convergence. Next, a new social action process which contains more operators and adaptive parameters is embedded in MACSAW. Finally, MACS2.1 lacks the weight vectors adjustment process which leads to diversity losing in some real problems and MACSAW adds it. Further, MACSAW is compared with some state-of-art algorithms and MACS2.1 on some standard benchmarks. It gets competitive results. Two real optimization problems is tackled and the results are analyzed in details.
06 Apr 2023Submitted to Expert Systems
01 Sep 2023Submission Checks Completed
01 Sep 2023Assigned to Editor
05 Sep 2023Reviewer(s) Assigned
22 Oct 2023Review(s) Completed, Editorial Evaluation Pending
29 Oct 2023Editorial Decision: Revise Major
05 Jun 20241st Revision Received
07 Jun 2024Submission Checks Completed
07 Jun 2024Assigned to Editor
24 Jun 2024Reviewer(s) Assigned
17 Jul 2024Review(s) Completed, Editorial Evaluation Pending
31 Jul 2024Editorial Decision: Accept